1. Technical Field
The present invention relates to facial recognition technology, and more particularly to a facial recognition method for eliminating the effect of noise blur and environmental variations.
2. Related Art
Conventionally, authority control of a system, such as passing through an entrance control system or logging into a computer security system, a user must input their account ID and the corresponding password.
The account ID and the password may be input by manual operation or automatic operation via an identification (ID) card, such as a regular ID cards (contact-type ID cards) or an RFID ID card. Risks for manual operation include the account ID and password being forgotten or intercepted. Risks for ID cards include being stolen or illegally duplicated.
In order to avoid these problems, facial recognition has been broadly adopted to serve as an identification mechanism for authority control.
Facial recognition technology is divided into two stages. The first stage is the learning stage, and the second stage is the face recognizing stage. In the face learning stage a facial image of a user's face is captured and then converted into digital data through a digitization process, so as to record the features of the user's face in the form of digital data. In the face recognizing stage, a facial image of an unknown face to be recognized is also captured and then converted into digitized data through the aforementioned digitalization process, so as to reflect the features of the unknown face by the digitized data. Finally, two sets of digital data are compared to determine whether or not the features of the two faces are similar, so as to determine whether or not the unknown face matches user's face.
Approaches of converting a facial image into digitized data to be recognized include Principle Components Analysis, three-dimensional facial recognition, face-feature based recognition, sub-feature-vector comparison, etc. Those approaches have their own advantages and disadvantages. However, they share a common draw back. The environment in which the face to be recognized is located is considerably different from that in the face learning stage; or the facial image to be recognized contains noise blur. As a result, either these environmental factors or the noise blur may result in actually matched face failing to pass the facial recognition stage. To prevent users from frequently failing to pass facial recognition, a comparative threshold of the facial recognition must be lowered. However, a reduced comparative threshold means that a lower safety factor of access control is introduced, and a stranger is more likely to pass facial recognition successfully.